U.S. patent application number 15/385787 was filed with the patent office on 2018-05-31 for event driven extract, transform, load (etl) processing.
This patent application is currently assigned to Amazon Technologies, Inc.. The applicant listed for this patent is Amazon Technologies, Inc.. Invention is credited to PRAJAKTA DATTA DAMLE, GOPINATH DUDDI, ANURAG WINDLASS GUPTA, GEORGE STEVEN MCPHERSON, MEHUL A. SHAH.
Application Number | 20180150529 15/385787 |
Document ID | / |
Family ID | 62190254 |
Filed Date | 2018-05-31 |
United States Patent
Application |
20180150529 |
Kind Code |
A1 |
MCPHERSON; GEORGE STEVEN ;
et al. |
May 31, 2018 |
EVENT DRIVEN EXTRACT, TRANSFORM, LOAD (ETL) PROCESSING
Abstract
Extract, Transform, Load (ETL) processing may be initiated by
detected events. A trigger event may be associated with an ETL
process apply one or more transformations to a source data object.
The trigger event may be detected for the ETL process and evaluated
with respect to one or more execution conditions for the ETL
process. If the execution conditions for the ETL process are
satisfied, then the ETL process may be executed. At least some of
the source data object may be obtained, the one or more
transformations of the ETL process may be applied, and one or more
transformed data objects may be stored.
Inventors: |
MCPHERSON; GEORGE STEVEN;
(SEATTLE, WA) ; SHAH; MEHUL A.; (SARATOGA, CA)
; DAMLE; PRAJAKTA DATTA; (SAN JOSE, CA) ; DUDDI;
GOPINATH; (SAN JOSE, CA) ; GUPTA; ANURAG
WINDLASS; (ATHERTON, CA) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Amazon Technologies, Inc. |
Seattle |
WA |
US |
|
|
Assignee: |
Amazon Technologies, Inc.
Seattle
WA
|
Family ID: |
62190254 |
Appl. No.: |
15/385787 |
Filed: |
December 20, 2016 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
|
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62426574 |
Nov 27, 2016 |
|
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Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06F 9/542 20130101;
G06F 16/254 20190101 |
International
Class: |
G06F 17/30 20060101
G06F017/30; G06F 9/54 20060101 G06F009/54 |
Claims
1. A system, comprising: at least one processor; a memory to store
program instructions that, if executed, cause the at least one
processor to perform a method, comprising: detecting a trigger
event to execute an Extract, Transform, Load (ETL) process
comprising one or more transformations to be applied to a source
data object stored in a first data store; evaluating the trigger
event to determine whether the trigger event satisfies one or more
execution criteria for the ETL process; in response to determining
that the trigger event satisfies the execution criteria: obtaining
at least a portion of the source data object from the first data
store; applying the one or more transformations to the obtained
portion of the source data object according to the ETL process to
generate one or more transformed data objects; and storing the
transformed data objects in a second data store.
2. The system of claim 1, wherein the method further comprises:
receiving a request to register the trigger event for the ETL
process; and enabling monitoring for the trigger event according to
the registration of the trigger event, wherein the monitoring
detects the trigger event.
3. The system of claim 1, wherein detecting the trigger event to
execute the ETL process comprises detecting an update to a data
catalog for the source data object.
4. The system of claim 3, wherein the at least one processor and
the memory are implemented as part of an ETL service offered by a
provider network, wherein the data catalog is maintained as part of
the ETL service, wherein the update to the data catalog is a
received via a network-based interface for the ETL service, and
wherein the first data store and the second data store are
implemented as part of one or more data storage services offered by
the provider network.
5. A method, comprising: detecting a trigger event to execute an
Extract, Transform, Load (ETL) process comprising one or more
transformations to be applied to a source data object stored in a
first data store; evaluating the trigger event to determine whether
the trigger event satisfies one or more execution criteria for the
ETL process; in response to determining that the trigger event
satisfies the execution criteria: obtaining at least a portion of
the source data object from the first data store; applying the one
or more transformations to the obtained portion of the source data
object according to the ETL process to generate one or more
transformed data objects; and storing the transformed data objects
in a second data store.
6. The method of claim 5, wherein the method further comprises:
receiving a request to register the trigger event for the ETL
process; and enabling monitoring for the trigger event according to
the registration of the trigger event, wherein the monitoring
detects the trigger event.
7. The method of claim 5, wherein the trigger event for the ETL
process indicates that the source data object is a new data object
stored in the first data store.
8. The method of claim 5, wherein detecting the trigger event to
execute the ETL process comprises detecting an update to a data
catalog for the source data object.
9. The method of claim 5, wherein evaluating the trigger event
comprises determining whether a tag attribute for the source data
object matches one or more tag attribute values permitting
execution of the ETL process.
10. The method of claim 5, wherein evaluating the trigger event
comprises evaluating one or more data values of the source data
object with respect to a data value criteria of the one or more
execution criteria.
11. The method of claim 5, wherein evaluating the trigger event
comprises evaluating a status of a previously initiated ETL process
performed upon the source data object or another data object.
12. The method of claim 5, wherein the one or more execution
criteria are some of a plurality of execution criteria evaluated
with respect to the trigger event, wherein at least one other one
of the plurality of execution criteria is not satisfied with
respect to the trigger event.
13. The method of claim 5, wherein the first data store and the
second data store are implemented as part of one or more data
storage services offered by a provider network, wherein the
detecting the trigger event, the evaluating the triggering event,
the obtaining the portion of the source data object, the applying
the transformations, and the storing the transformed data objects
are performed by an ETL service offered by the provider
network.
14. A non-transitory, computer-readable storage medium, storing
program instructions that when executed by one or more computing
devices cause the one or more computing devices to implement:
detecting a trigger event to execute an Extract, Transform, Load
(ETL) process comprising one or more transformations to be applied
to a source data object stored in a first data store; evaluating
the trigger event to determine whether the trigger event satisfies
one or more execution criteria for the ETL process; in response to
determining that the trigger event satisfies the execution
criteria: obtaining at least a portion of the source data object
from the first data store; applying the one or more transformations
to the obtained portion of the source data object according to the
ETL process to generate one or more transformed data objects; and
storing the transformed data objects in a second data store.
15. The non-transitory, computer-readable storage medium of claim
14, wherein the source data object is a table and wherein the
trigger event for the ETL process indicates that a new partition of
data for the table is stored in the first data store.
16. The non-transitory, computer-readable storage medium of claim
14, wherein, in detecting the trigger event to execute the ETL
process, the program instructions cause the one or more computing
devices to implement detecting an update to a data catalog for the
source data object.
17. The non-transitory, computer-readable storage medium of claim
14, wherein evaluating the trigger event comprises evaluating a
size of the source data object with respect to a size threshold as
one of the one or more execution criteria.
18. The non-transitory, computer-readable storage medium of claim
14, wherein the program instructions cause the one or more
computing devices to further implement: receiving a request to
register the trigger event for the ETL process; and in response to
receiving the request to register the trigger event, enabling
monitoring for the trigger event, wherein the monitoring detects
the trigger event.
19. The non-transitory, computer-readable storage medium of claim
18, further comprising: prior to performing the obtaining, the
applying, and the storing, attempting to execute the ETL process,
wherein the attempt to execute the ETL process fails; performing
the obtaining, the applying, and the storing according to a retry
configuration for the ETL process, wherein the retry configuration
directs one or more further attempts to execute the ETL process
without detecting another trigger event for the ETL process.
20. The non-transitory, computer-readable storage medium of claim
14, wherein the program instructions cause the one or more
computing devices to further implement: upon completion of the ETL
process, triggering another trigger event for another ETL process
performed upon the source data object or the transformed data
objects.
Description
RELATED APPLICATIONS
[0001] This application claims benefit of priority to U.S.
Provisional Application Ser. No. 62/426,574, entitled "Event Driven
Extract, Transform, Load (ETL) Processing," filed Nov. 27, 2016,
and which is incorporated herein by reference in its entirety.
BACKGROUND
[0002] As the technological capacity for organizations to create,
track, and retain information continues to grow, a variety of
different technologies for managing and storing the rising tide of
information have been developed. Database systems, for example,
provide clients with many different specialized or customized
configurations of hardware and software to manage stored
information. The increasing amount of data that organizations must
store and manage often correspondingly increases both the size and
complexity of data storage and management technologies, like
database systems, which in turn escalate the cost of maintaining
the information. New technologies seek to reduce both the
complexity and storage requirements of maintaining data by
introducing different data formats that offer different processing
or maintenance capabilities. However, introducing multiple data
formats is not without cost. Data is often processed by different
systems which may not support the current data format of the data.
Thus, the ability to perform techniques that extract, transform,
and load data between different formats or locations is
desirable.
BRIEF DESCRIPTION OF THE DRAWINGS
[0003] FIG. 1 illustrates a logical block diagram of event driven
extract, transform, load (ETL) processing, according to some
embodiments.
[0004] FIG. 2 is a block diagram illustrating a provider network
offering different services including an extract, transform, load
(ETL) service that performs event driven ETL processing, according
to some embodiments.
[0005] FIG. 3 is a block diagram illustrating an ETL service that
performs event driven ETL processing, according to some
embodiments.
[0006] FIG. 4 is a logical block diagram illustrating ETL job
management, according to some embodiments.
[0007] FIG. 5 is a logical block diagram illustrating interactions
to execute an ETL job, according to some embodiments.
[0008] FIG. 6 illustrates example interactions to register a
trigger event for an ETL job, according to some embodiments.
[0009] FIG. 7 is a high-level flowchart illustrating methods and
techniques to implement event driven ETL processing, according to
some embodiments.
[0010] FIG. 8 illustrates an example system configured to implement
the various methods, techniques, and systems described herein,
according to some embodiments.
[0011] While embodiments are described herein by way of example for
several embodiments and illustrative drawings, those skilled in the
art will recognize that embodiments are not limited to the
embodiments or drawings described. It should be understood, that
the drawings and detailed description thereto are not intended to
limit embodiments to the particular form disclosed, but on the
contrary, the intention is to cover all modifications, equivalents
and alternatives falling within the spirit and scope as defined by
the appended claims. The headings used herein are for
organizational purposes only and are not meant to be used to limit
the scope of the description or the claims. As used throughout this
application, the word "may" is used in a permissive sense (i.e.,
meaning having the potential to), rather than the mandatory sense
(i.e., meaning must). Similarly, the words "include," "including,"
and "includes" mean including, but not limited to.
[0012] It will also be understood that, although the terms first,
second, etc. may be used herein to describe various elements, these
elements should not be limited by these terms. These terms are only
used to distinguish one element from another. For example, a first
contact could be termed a second contact, and, similarly, a second
contact could be termed a first contact, without departing from the
scope of the present invention. The first contact and the second
contact are both contacts, but they are not the same contact.
DETAILED DESCRIPTION OF EMBODIMENTS
[0013] Various embodiments of event driven extract, transformation,
and load (ETL) processing are described herein. ETL processing
provides data administrators, stake holders, producers, or
consumers with the ability to take advantage of different data
formats, storage systems, or storage locations by facilitating the
movement data across different data stores and data schemas or
formats. For example, data producers that collect usage and other
analytics for web site interactions (e.g., visitor clicks and other
actions), may generate stored analytics data in large log files or
other semi-structured data formats. However, in order to perform
various analytical operations or queries over the analytics data,
an ETL process may be performed to extract desired data,
transformed the desired data into a format usable by an analytics
engine, like a database system, and load the extracted data into
the appropriate storage system in the appropriate data schema and
format.
[0014] Typical ETL processing techniques rely upon chronological
mechanisms for initiating the execution of ETL processing. An ETL
process may be scheduled to execute nightly, or once a week at a
certain time on a certain day. However, because ETL processing jobs
interact with and transform data, the timing for executing ETL
processing jobs may be optimally aligned with changes to data or
events respecting data as data is not always created, updated,
modified, or deleted upon a schedule, nor created, updated,
modified, or deleted in the same way. For instance, new data
objects may be created or stored in a data store to add another
day's worth of sales data or may be stored as corrections to a
larger data set. Event driven ETL processing may discriminate
between optimal ETL execution scenarios and non-optimal ETL
execution scenarios to account for the differing nature of changes
to data.
[0015] FIG. 1 illustrates a logical block diagram of event driven
extract, transform, load (ETL) processing, according to some
embodiments. Data store(s) 110 may store data objects on behalf of
one or multiple clients. Data objects may be may any form of data
or data structure, (e.g., file, directory, database table, log,
block, partition, chunk, or range of bytes). Various data events
120 may occur in a data store with respect to stored data objects.
For example, data creation events 122 may result from the storing
of new data objects. A data producer, for example, may upload a new
log file into data store(s) 120, creating a new data object. In
another example, a new partition for database table may be added.
Data modification event(s) 124 may occur when data is updated or
transformed to create other data objects. For instance, an
aggregation operation may be performed to access raw data stored in
a data object, aggregate the values of different fields, and store
the aggregated values in another data object (e.g., calculate total
sales from a data object storing daily transactions and store the
total sales for the day in a new entry in a revenue table). Data
deletion events 126 may occur to archive, move, or remove data
objects from data store(s) 110. For example, raw data objects may
be discarded once a cleaned version of the data object is
created.
[0016] As illustrated in FIG. 1, different data events 120 may
serve as trigger events for ETL process 150 that are executed by
ETL job processing 130. For example, a trigger event (e.g.,
triggered by a specified data event 120) may be associated with an
ETL process (e.g. in a trigger event registry as discussed below
with regard to FIG. 4). ETL job processing 130 may detect the
occurrence of the trigger event 150 and determine whether the ETL
process should be executed using execution criteria specified for
the ETL process. For example, ETL job processing may include
execution criteria analysis 140 to compare various attributes of a
source data object for the ETL process with different criteria
(e.g., size of the source data object with a threshold execution
size, number of items, such as rows, within the source data object,
with a row threshold, etc.). Other execution criteria, such as
those discussed below with regard to FIGS. 4, 6, and 7 may be
evaluated, including criteria for evaluating the status of other
ETL processes.
[0017] If the execution criteria are satisfied, then ETL job
processing 130 may execute the triggered ETL process 160. For
example, ETL job processing 130 may obtain portions of a source
data object in data store(s) 110, apply different transformations
as part of the ETL processing, such as transformations that
convert, combine or otherwise modify data values obtained from the
source data object, remove, filter or drop data values obtained
from the source data object, or restructure, rearrange, or relocate
data values obtained from the source data object. The results of
the applied transformations may be included in one or more
transformed data objects which may be stored in data store(s) 110.
ETL job processing 130 may create different data events as a result
of executing ETL processes and thus may initiate subsequent ETL
processing.
[0018] Please note that the previous description of event driven
ETL processing is a logical illustration and thus is not to be
construed as limiting as to the architecture for implementing a
data store or ETL job processing.
[0019] This specification begins with a general description of a
provider network that implements an extract, transform, load (ETL)
service that identifies, transforms, and moves data stored in the
provider network or in external data stores. Then various examples
of the ETL service including different components/modules, or
arrangements of components/module that may be employed as part of
implementing the ETL service are discussed. A number of different
methods and techniques to implement event driven ETL processing are
then discussed, some of which are illustrated in accompanying
flowcharts. Finally, a description of an example computing system
upon which the various components, modules, systems, devices,
and/or nodes may be implemented is provided. Various examples are
provided throughout the specification.
[0020] FIG. 2 is a block diagram illustrating a provider network
offering different services including an extract, transform, load
(ETL) service that performs event driven ETL processing, according
to some embodiments. Provider network 200 may be a private or
closed system or may be set up by an entity such as a company or a
public sector organization to provide one or more services (such as
various types of cloud-based storage) accessible via the Internet
and/or other networks to clients 250. Provider network 200 may be
implemented in a single location or may include numerous data
centers hosting various resource pools, such as collections of
physical and/or virtualized computer servers, storage devices,
networking equipment and the like (e.g., computing system 1000
described below with regard to FIG. 8), needed to implement and
distribute the infrastructure and storage services offered by the
provider network 200. In some embodiments, provider network 200 may
implement various computing resources or services, such as a data
storage service(s) 210 (e.g., object storage services, block-based
storage services, or data warehouse storage services), ETL service
220, as well as other service(s) 230, which may include a virtual
compute service, data processing service(s) (e.g., map reduce, data
flow, and/or other large scale data processing techniques), and/or
any other type of network based services (which may include various
other types of storage, processing, analysis, communication, event
handling, visualization, and security services not
illustrated).
[0021] In various embodiments, the components illustrated in FIG. 2
may be implemented directly within computer hardware, as
instructions directly or indirectly executable by computer hardware
(e.g., a microprocessor or computer system), or using a combination
of these techniques. For example, the components of FIG. 2 may be
implemented by a system that includes a number of computing nodes
(or simply, nodes), each of which may be similar to the computer
system embodiment illustrated in FIG. 8 and described below. In
various embodiments, the functionality of a given system or service
component (e.g., a component of data storage service 230) may be
implemented by a particular node or may be distributed across
several nodes. In some embodiments, a given node may implement the
functionality of more than one service system component (e.g., more
than one data store component).
[0022] Data storage service(s) 210 may implement different types of
data stores for storing, accessing, and managing data on behalf of
clients 250 as a network-based service that enables clients 250 to
operate a data storage system in a cloud or network computing
environment. For example, data storage service(s) 210 may include
various types of database storage services (both relational and
non-relational) or data warehouses for storing, querying, and
updating data. Such services may be enterprise-class database
systems that are scalable and extensible. Queries may be directed
to a database or data warehouse in data storage service(s) 210 that
is distributed across multiple physical resources, and the database
system may be scaled up or down on an as needed basis. The database
system may work effectively with database schemas of various types
and/or organizations, in different embodiments. In some
embodiments, clients/subscribers may submit queries in a number of
ways, e.g., interactively via an SQL interface to the database
system. In other embodiments, external applications and programs
may submit queries using Open Database Connectivity (ODBC) and/or
Java Database Connectivity (JDBC) driver interfaces to the database
system.
[0023] Data storage service(s) 210 may also include various kinds
of object or file data stores for putting, updating, and getting
data objects or files, which may include data files of unknown file
type. Such data storage service(s) 210 may be accessed via
programmatic interfaces (e.g., APIs) or graphical user interfaces.
Data storage service(s) 210 may provide virtual block-based storage
for maintaining data as part of data volumes that can be mounted or
accessed similar to local block-based storage devices (e.g., hard
disk drives, solid state drives, etc.) and may be accessed
utilizing block-based data storage protocols or interfaces, such as
internet small computer interface (iSCSI).
[0024] In some embodiments, ETL service 220 may create and
dynamically update a catalog of data stored on behalf of clients in
provider network 200 across the various data storage services 210,
as discussed in detail below with regard to FIG. 3. For example, a
database stored in a non-relational database format may be
identified along with container storing objects in an object-based
data store as both being stored on behalf of a same customer of
provider network 200. ETL service 220 may also perform ETL jobs
that extract, transform, and load from one or more of the various
data storage service(s) 210 to another location. For example, ETL
service 220 may provide clients with the resources to create,
maintain, and orchestrate data loading jobs that take one or more
data sets, perform various transformation operations, and store the
transformed data for further processing (e.g., by one or more of
data processing service(s)). ETL service 220 may access a data
catalog generated by ETL service 220 in order to perform an ETL
operation (e.g., a job to convert a data object from one file type
into one or more other data objects of a different file type). As
discussed in detail below with regard to FIGS. 3-5, ETL service 220
may generate transformation workflows on behalf of clients
automatically.
[0025] Other service(s) 230 may include various types of data
processing services to perform different functions (e.g., anomaly
detection, machine learning, querying, or any other type of data
processing operation). For example, in at least some embodiments,
data processing services may include a map reduce service that
creates clusters of processing nodes that implement map reduce
functionality over data stored in one of data storage services 210.
Various other distributed processing architectures and techniques
may be implemented by data processing services (e.g., grid
computing, sharding, distributed hashing, etc.). Note that in some
embodiments, data processing operations may be implemented as part
of data storage service(s) 210 (e.g., query engines processing
requests for specified data). Data processing service(s) may be
clients of ETL service 220 in order to invoke the execution of an
ETL job to make data available for processing in a different
location or data format for performing various processing
operations with respect to data sets stored in data storage
service(s) 210.
[0026] Generally speaking, clients 250 may encompass any type of
client configurable to submit network-based requests to provider
network 200 via network 260, including requests for storage
services (e.g., a request to create, read, write, obtain, or modify
data in data storage service(s) 210, a request to generate an ETL
job at ETL service 220, etc.). For example, a given client 250 may
include a suitable version of a web browser, or may include a
plug-in module or other type of code module configured to execute
as an extension to or within an execution environment provided by a
web browser. Alternatively, a client 250 may encompass an
application such as a database application (or user interface
thereof), a media application, an office application or any other
application that may make use of storage resources in data storage
service(s) 210 to store and/or access the data to implement various
applications. In some embodiments, such an application may include
sufficient protocol support (e.g., for a suitable version of
Hypertext Transfer Protocol (HTTP)) for generating and processing
network-based services requests without necessarily implementing
full browser support for all types of network-based data. That is,
client 250 may be an application configured to interact directly
with provider network 200. In some embodiments, client 250 may be
configured to generate network-based services requests according to
a Representational State Transfer (REST)-style network-based
services architecture, a document- or message-based network-based
services architecture, or another suitable network-based services
architecture.
[0027] In some embodiments, a client 250 may be configured to
provide access to provider network 200 to other applications in a
manner that is transparent to those applications. For example,
client 250 may be configured to integrate with an operating system
or file system to provide storage on one of data storage service(s)
210 (e.g., a block-based storage service). However, the operating
system or file system may present a different storage interface to
applications, such as a conventional file system hierarchy of
files, directories and/or folders. In such an embodiment,
applications may not need to be modified to make use of the storage
system service model. Instead, the details of interfacing to the
data storage service(s) 210 may be coordinated by client 250 and
the operating system or file system on behalf of applications
executing within the operating system environment.
[0028] Clients 250 may convey network-based services requests
(e.g., access requests directed to data in data storage service(s)
210, operations, tasks, or jobs, being performed as part of other
service(s) 230, or to interact with ETL service 220) to and receive
responses from provider network 200 via network 260. In various
embodiments, network 260 may encompass any suitable combination of
networking hardware and protocols necessary to establish
network-based-based communications between clients 250 and provider
network 200. For example, network 260 may generally encompass the
various telecommunications networks and service providers that
collectively implement the Internet. Network 260 may also include
private networks such as local area networks (LANs) or wide area
networks (WANs) as well as public or private wireless networks. For
example, both a given client 250 and provider network 200 may be
respectively provisioned within enterprises having their own
internal networks. In such an embodiment, network 260 may include
the hardware (e.g., modems, routers, switches, load balancers,
proxy servers, etc.) and software (e.g., protocol stacks,
accounting software, firewall/security software, etc.) necessary to
establish a networking link between given client 250 and the
Internet as well as between the Internet and provider network 200.
It is noted that in some embodiments, clients 250 may communicate
with provider network 200 using a private network rather than the
public Internet.
[0029] FIG. 3 is a block diagram illustrating an ETL service that
performs event driven ETL processing, according to some
embodiments. ETL service 220 may provide access to data catalogs
360 and ETL jobs (for creation, management, and execution) via
interface 310, which may be a programmatic interface (e.g.,
Application Programming Interface (API)), command line interface,
and/or graphical user interface, in various embodiments.
[0030] ETL Service 220 may implement ETL job creation 320 to handle
the creation of ETL jobs through manual job creation (e.g.,
creating, edit, or uploading ETL code or creating or editing graphs
of ETL jobs) or through automated job creation. ETL job creation
320 may handle requests for automated ETL job creation and manual
ETL job creation. For example, ETL job creation 320 may receive job
generation request which may specify the data object and target
data format for the ETL job. Other job information, such as access
credentials, triggering events, or any other information to execute
the ETL job may be included as part of the creation request or as
part of a trigger event registration request, discussed below with
regard to FIG. 6. ETL job creation 320 may automatically generate
ETL code to perform an ETL job by determining the source data
format of the data object and the target data format of the data
object. For example, in one embodiment, the source and target data
formats from data catalog 360. In another embodiment, data format
identification may perform data format recognition techniques, or
access other data stores (e.g., such as a data catalog stored in
relational database) to retrieve the data format information. ETL
job creation 320 may then compare the source data format and target
data format or schema to select transformations to apply to the
source data object to achieve the target data format. ETL job
creation 320 may then generate code for selected transformations
and construct the source code for executing the selected
transformations. The code for the ETL job may be stored in ETL job
store 350 for subsequent execution.
[0031] ETL job creation 320 may also implement manual creation of
ETL jobs. For example, transformation operations may be manually
selected, combined, or assembled via graphical user interface to
define a workflow of transformations to apply. Code corresponding
to the workflow may be generated (or supplied by a user), edited,
and stored for subsequent execution as part of ETL job store
350.
[0032] ETL service 220 may implement ETL job management 330 to
provide clients with the ability to manage, edit, delete, or
otherwise change ETL jobs. Trigger events, may also be defined for
ETL jobs (as discussed below with regard to FIG. 6). ETL job
management 330 may monitor for trigger events and request execution
of ETL jobs, as discussed below with regard to FIG. 4.
[0033] ETL service 220 may implement ETL job execution 340 to
provide an execution platform ETL jobs. In some embodiments, ETL
job execution 340 may provide a serverless architecture (from the
perspective of clients) so that the appropriate number of resources
are provisioned (e.g., virtual compute instances from a virtual
compute service executing the ETL job code) in order to satisfy
performance requirements, objectives, or goals provided by a client
or by ETL service 220. ETL job execution 340 may execute jobs, in
some embodiments, automatically without any user editing changes to
the automatically generated ETL code from ETL job creation 320. In
some embodiments, ETL job execution 340 may execute automatically
generated ETL jobs that were modified. ETL job execution 340 may
execute jobs in response to detected triggering events for ETL jobs
(which may be detected by ETL job management or another system or
service monitoring for triggering event conditions), as discussed
below with regard to FIG. 5.
[0034] ETL service 220 may maintain data catalogs 360 that describe
data sets (stored in provider network 200 or in external storage
locations). ETL service 220 may identify unknown data objects,
identify a data format for the unknown data objects and store the
data format in a data catalog for the unknown data objects. ETL
service 220 allow for catalog users, owners, or other stakeholders,
to modify or otherwise manage data catalogs. For instance, ETL
service 220 may process and execute access requests directed to
data catalog(s) 360 (e.g., requests to combine, delete, or split
tables of metadata in the catalog or edit the metadata determined
for a data catalog). ETL service 220 may implement access or
control policies for data catalogs (e.g., to limit access to a data
catalog to authorized users). ETL service 220 may implement data
retention or life cycle policies to determine how long data
catalogs (or older versions of data catalogs) are maintained. ETL
service 220 may handle the provisioning of storage resources in
data for creating new data catalogs. ETL service 220 may also
perform load balancing, heat management, failure recovery, and
other resource management techniques (e.g., implement durability
requirements) to ensure the availability of data catalogs for
clients.
[0035] Storage for data catalog(s) 360 may be implemented by one or
more storage nodes, services, or computing devices (e.g., system
1000 discussed below with regard to FIG. 8) to provide persistent
storage for data catalogs generated by data catalog service 200.
Such storage nodes (or other storage components of storage for data
catalog(s) 360) may implement various query processing engines or
other request handling components to provide access to data
catalogs according to requests received via interface 310. For
example, data catalog storage may be implemented as a
non-relational database, in one embodiment, that stores file types
and other metadata for data objects in table. In some embodiments,
collections of metadata for various data objects stored across
different storage service(s) 210 on behalf a single user account
may be stored together in a single catalog of metadata that may be
made accessible to clients.
[0036] FIG. 4 is a logical block diagram illustrating ETL job
management, according to some embodiments. ETL job management 330
may monitor for trigger events for ETL jobs and request the
execution of ETL jobs that satisfy execution criteria for the ETL
job. ETL job management 330 may implement trigger event registry
430, in various embodiments. Trigger event registry 430 may be a
data store, such as database, or other data storage system that
tracks the trigger events 432 for which monitoring is enabled, as
well as the execution criteria 434 for evaluating whether an ETL
job may be performed. Trigger event registry 430 may be updated by
requests 470 to register or update trigger events, as discussed in
detail below with regard to FIG. 6. For example, a request 470 to
register a trigger event may identify the ETL job, type of trigger
event, and execution criteria to be applied for determining whether
execution even of the ETL job when the trigger event is detected
can proceed.
[0037] ETL job management 330 may implement trigger event
monitoring 410 to detect trigger events for ETL jobs. For example,
trigger event monitoring 410 may implement listeners or other
processes that wait and identify events for a specified ETL job.
For example, trigger event monitoring 410 may enable a listener for
an ETL job that operates upon data source X by configuring the
listener to identify updates 440 to data source X amongst update
notifications provided to ETL job management 330. In some
embodiments, trigger event monitoring 410 may monitor, get, or
evaluate data object updates 450 provided 440 to data catalog 360.
For example, ETL service 220 may make an interface to data catalog
360 available so that when a data producer, modifier, or other
process that accesses a data object in a data store performs an
action, a description of the update or a resulting update to the
data objects metadata or data schema is provided 440 to data
catalog 360 via the interface (e.g., via an API request to report
the update). Trigger event monitoring 410 may subscribe to a feed
or stream of updates (e.g., particular to a data object in the data
catalog or the data catalog as a whole) in order to monitor the
data object updates for trigger events. For other types of trigger
events, trigger event monitoring may implement other listeners,
scanners, or handlers to retrieve information about ETL process
execution status, for example, or to implement a schedule for
time-based trigger events.
[0038] Execution criteria evaluation 420 may lookup execution
criteria 434 for detected trigger events 454 to determine whether
the ETL job for the trigger even should be executed. For example,
in various embodiments, execution criteria may include criteria
with respect to a source data object for the ETL job (e.g., size,
data values, or other metadata or attributes of the data object).
Execution criteria evaluation 420 may obtain the needed information
from data catalog 360, such as data object attributes 460 (e.g.,
data object size, number of rows added, etc.) to evaluate execution
criteria. Although not illustrated, information to evaluate other
execution criteria (e.g., by accessing ETL job status information
or data objects directly) may be obtained, in different
embodiments, to evaluate execution criteria. For those trigger
events that are evaluated and satisfy the execution criteria, an
ETL job execution request 480 may be provided to ETL job execution
service 340 to execute the job identified by the request 480.
[0039] FIG. 5 is a logical block diagram illustrating interactions
to execute an ETL job, according to some embodiments. ETL job
execution 340 may implement an ETL job execution coordinator 510
that assigns ETL jobs 512 to one or more ETL job execution
workers(s) 520 which may access source data store 530 and target
data store 540 to obtain data 524, apply transformations, and store
transformed data 526. ETL job execution request 502 may identify
the job to execute (e.g., by include a job name or other
identifier). ETL job execution coordinator 510 may determine the
resources needed to execute the ETL job and assign the ETL job to
one or more ETL job execution worker(s) 520.
[0040] ETL Job execution worker(s) 520 may get information 522
(including executable code, invoked operations or transformations,
and other information to execute the identified ETL job) from ETL
job store 350 for the ETL job. ETL job execution worker(s) 520 may
then perform the ETL job in parallel or serialized fashion,
obtaining data 524 from the source data store 530 (which may be a
data storage service 210 of provider network 200). For example, ETL
job execution worker(s) 520 may establish a connection to transfer
data from source data store 530 and send one or more requests to
obtain some or all of a source data object (e.g., via API requests
for the source data store or via storage or transfer protocol, like
secure file transfer protocol (SFTP) or an internet Small Computer
Systems Interface (iSCSI)). Job execution worker(s) 520 may then
apply the various transformation(s) or other operations specified
by the ETL job to the obtained data.
[0041] Various transformations may be applied by ETL job execution
worker(s) 520 and may include, but are not limited to, dropping one
or more fields, values, or items from the source data object,
converting data into a relational data format (e.g., converting
lists, items or attributes, into row entries with respective column
values), renaming a column, field, or attribute, selecting
particular fields from the data object, or splitting fields into
two different frames, locations, fields, or attributes, splitting
rows, entries, or items into separate rows, entries, or items,
unboxing or boxing data values, like strings, aggregating or
combining data values, reducing the fidelity of data values by
converting, rounding, truncating or modifying data values, or
recognizing and generating custom data values (e.g., that combine
values of multiple different types).
[0042] ETL job execution worker(s) 520 may establish a connection
to store transformed data 526 into target data store 540 (which may
be the same or different as source data store 530) via API requests
for target data store 540 or via storage or a transfer protocol,
like SFTP or iSCSI. ETL job execution worker(s) 520 may also access
and update an ETL job execution log 550 with job execution status
528. For example, ETL job execution workers may identify the
progress of the ETL job (e.g., X rows out of Y total rows in a
source table processed). In this way, failures of ETL job execution
worker(s) 520 may be recovered from by access ETL job execution log
550 to determine the last processed portion of a source data
object. Errors and other events may be recorded for the ETL job in
ETL job execution log 550, in some embodiments. ETL job execution
worker(s) 520 may send trigger events 562 indicating completion of
the ETL job to ETL job management 330, in some embodiments, which
may be a trigger event or execution criteria for other ETL
jobs.
[0043] FIG. 6 illustrates example interactions to register a
trigger event for an ETL job, according to some embodiments. As
noted above, interface 310 may be a network-based, graphical,
and/or programmatic interface (e.g., API), that allows clients,
such as client 610 (which may be similar to clients 250 discussed
above with regard to FIG. 2), access to ETL service 220. Client 610
may submit a request to register a trigger event 610 to ETL job
management 330 via interface 310. The register trigger event
request 610 may be sent as part of a request to create an ETL job
or process for execution, or separately. Different information may
be included in registration request 610 (or may be included in
separate requests, not illustrated).
[0044] For example, register trigger event request 620 may include
information to identify a source data object from which an ETL
process may execute upon. An object identifier, name, file path, or
other location may be used to specify the source data object. In
some embodiments, multiple source data object(s) may be identified.
Register trigger event request 620 may identify the target data
object(s) to store transformed data objects as part of executing
the ETL process. For example, target data object(s) may be
identified by pointing a location, such as file directory, or a
data object to add the target data object(s) to (e.g., as new
partitions).
[0045] Register trigger event request 620 may identify the ETL
process for which the trigger event applies, in various
embodiments. For instance, the registration request may include a
job identifier, such as a job name, pointer, index entry, or other
location where the ETL process to execute may be retrieved.
Register trigger event 620 may identify a type of trigger event for
the ETL process. For example, a trigger event may be identified as
an event triggered by events associated with data (whether the data
is the source data object or another data object), including the
creation, modification, or deletion of data. For example, when a
new data object is stored, the arrival of the new data may trigger
an event. Another trigger event type may be based on the
performance or completion of another ETL job or process. For
example, the completion of an ETL process to create a filtered data
set may trigger the execution of an ETL process that uses the
filtered data set as a source data set. Trigger events may also be
time-based events. For example, a time-based trigger event may
schedule the execution of the ETL process at a particular time of
day, day of the week, day of the month, or day of the year.
[0046] Register trigger event request 620 may also include the
various kinds of execution criteria that may be evaluated with
respect to the trigger event. For example, execution criteria may
evaluate the source data object upon which the ETL process performs
by specifying data values, fields, rows, metadata (including tags,
labels, size, or other data object attributes), as well as
different calculations and/or comparisons to evaluate the execution
criteria. Data size thresholds, for instance, may be evaluated,
permitting or denying execution of the ETL process depending on the
size of the source data object. In another example, an execution
criterion may determine and compare the number of rows or
attributes added as part of the source data object with row
threshold. Execution criteria may include other criteria, such as
evaluations of the execution status of other ETL processes,
time-based evaluations (e.g., time elapsed since last execution of
the ETL process), or any other evaluation to permit or deny
execution of the ETL process.
[0047] In some embodiments, register trigger event request 620 may
provide further information for the execution of the ETL process,
such as access credentials (e.g., to obtain access to data stores),
configuration of logging for the ETL process (e.g., identifying a
log store or file for storing an execution for the ETL process), as
well as other execution configuration information (e.g., retry
attempts if the ETL process fails, timeout period for attempting to
access data stores, etc.).
[0048] Client 610 may also send a request to modify a trigger event
630 to ETL job management 330 via interface 310. For example, the
trigger event type may be changed (from a data-based trigger event
to a time-based trigger event or a process-based trigger event).
Changes may be made to the execution criteria. For example,
thresholds or conditions may be altered, additional execution
criteria added or execution criteria removed. Execution criteria
may be modified to change which execution criteria have to be
satisfied in order to execute the ETL job. For example, execution
criteria may originally require that criteria A, B, and C all be
satisfied to execute the ETL job. The modification request 630 may
change the execution criteria to require that criteria A be
satisfied along with at least one of criteria B or C.
[0049] Client 610 may also send a request to delete a trigger event
640 to ETL job management 330 via interface 310. Deletion of a
trigger event may not delete the ETL job, source data object, or
target data objects. But may disable monitoring for a trigger event
for the ETL job.
[0050] Although FIGS. 2-6 have been described and illustrated in
the context of an ETL service, the various techniques and
components illustrated and described in FIGS. 2-6 may be easily
applied to other data access or management systems in different
embodiments that may facilitate ETL processing operations.
Stand-alone ETL processing systems are an example of another
embodiment that may be implemented in private networks or systems
to perform similar techniques to those described above. As such,
FIGS. 2-6 are not intended to be limiting as to other embodiments
of a system that may implement event driven ETL processing. FIG. 7
is a high-level flowchart illustrating methods and techniques to
implement event driven ETL processing, according to some
embodiments. Various different systems and devices may implement
the various methods and techniques described below, either singly
or working together. For example, an ETL service such as described
above with regard to FIGS. 2-6 may be configured to implement the
various methods. Alternatively, a combination of different systems
and devices, such as a storage subsystem that manages access to
data stored in directly attached storage devices may transform data
using ETL processing the below techniques. Therefore, the above
examples and or any other systems or devices referenced as
performing the illustrated method, are not intended to be limiting
as to other different components, modules, systems, or
configurations of systems and devices.
[0051] As indicated at 710, a request may be received to register a
trigger event for an Extract, Transform, Load (ETL) process that
applies transformation(s) to a source data object in a first data
store, in some embodiments. The request may be received via a
programmatic interface, such as discussed above according to FIG. 6
and may indicate various information to associate with or configure
the trigger event. For example, the request may indicate or specify
the source data object (or multiple source data objects) from which
data is obtained upon which ETL processing is performed, the target
data object(s) generated as a result of the ETL processing (e.g.,
new partitions of table or new files in a directory), the type of
trigger event (e.g., data arrival, data creation, data update,
etc.), execution criteria (e.g., criteria with respect to the
source data object, criteria with respect to the timing of the
trigger event, criteria with respect to the execution status of
other ETL processing jobs), access credentials for executing the
ETL process (e.g., identity tokens, username/password information,
security role or other information to access source or target data
objects), logging configuration (e.g., identifying a location for
an event log for the ETL process), or execution configuration
(e.g., indicating the number of attempts to execute the ETL process
in the event of failure).
[0052] As indicated at 720, in response to receiving the request
monitoring may be enabled for the trigger event. For example, in
one embodiment, a monitoring agent or listener may be created to
receive updates or status changes to the source data object (e.g.,
such as updates to a data catalog for the source data object, as
discussed above with regard to FIG. 4). The updates may be
monitored to detect a trigger event. In another embodiment, a
periodic (or aperiodic) scan, poll, or other check may be performed
with respect to the source data object. For example, a header or
other metadata for the source data object may be read to determine
whether the source data object has been modified since a last
check. Monitoring for trigger events may be enabled differently for
different types of triggering events. For trigger events that are
triggered by a time (e.g., daily, weekly, or monthly process
schedule, or time elapsed since a last ETL process was initiated,
attempted, or completed), monitoring may include implementing a
timer or other mechanism to determine whether the trigger event
occurred.
[0053] As indicated at 730, a trigger event may be detected, in
some embodiments. For example, a new data object may be stored in a
location in the data store or an addition to or modification of
data may be performed with respect to a data object in the data
store. In at least some embodiments, the source data object may be
a table or other data object that includes multiple partitions and
the detected trigger event may be the arrival of an additional
partition for the table.
[0054] As indicated at 740, the trigger event may be evaluated with
respect to execution criteria for the ETL process. As noted above,
execution criteria may allow a data administrator to tailor the
execution of the ETL process to different circumstances or events
for data, such as various stages in a data life cycle. Execution
criteria may include any condition permitting or denying execution
of the ETL process. For example, execution criteria may be used to
evaluate the source data object, such as the size, format, or other
attribute. An execution criterion may deny execution of the ETL
process for source data objects less than 512 megabytes in size, in
one embodiment.
[0055] In at least some embodiments, metadata, such as user-defined
tags, labels, or other descriptors may be evaluated with respect to
execution criteria. For example, data objects tagged "test" or
"correction" may be compared with a set of tags (e.g.,
"production," "complete," or "results"). If the tag matches one of
the tags in the set, then the execution criteria may be satisfied.
In some embodiments, data values within the source data object may
be evaluated with respect to a data value criterion. For example,
an average or standard deviation may be calculated for field values
in one or more columns of a data object (e.g., table). If the
average or standard deviation is greater than a threshold value,
then the execution criterion may be satisfied. In at least some
embodiments, execution criteria may include evaluations of other
ETL processes, such as the execution status of an ETL process
(e.g., failed, completed, paused, etc.). For example, one execution
criterion may be an evaluation as to whether an ETL process applied
to the source data object (or another data object), completed
successfully.
[0056] Different combinations of execution criteria may be
specified for a trigger event. In some embodiments, execution
criteria may be specified in a request to register the trigger
event. In other embodiments, default execution criteria (e.g.,
dependent on the trigger event type) may be assigned to the trigger
event. In some embodiments, combinations of execution criteria may
be satisfied or not satisfied, and execution of the ETL process may
still be allowed. For example, execution criteria may include a
further criterion that permits execution of the ETL process if 2 or
more execution criteria out of a larger group of execution criteria
are satisfied.
[0057] If execution criteria are not satisfied, as indicated by the
negative exit from 750, then monitoring for the trigger event may
continue. For trigger events that satisfy the execution criteria,
the ETL process may be executed. For example, as indicated at 760,
at least a portion of the source data object may be obtained from
the first data store. Different records, files, partitions or other
portions may be read from the first data store for processing. As
indicated at 770, the transformation(s) may be applied to the
obtained portion according to the ETL process in order to generate
one or more transformed data object(s). For example, different
transformations to filter, modify, rearrange, combine, delete,
convert, other any other change to the portion may be performed to
generate the transformed data objects. Transformed data may be
split into multiple data objects or stored in a single transformed
data objects. In some embodiments, transformed data object(s) may
be a partition or other addition to an existing data set (e.g., a
partition for an existing table).
[0058] Once the transformed data object(s) are generated, the
transformed data objects may be stored in a second data store, as
indicated at 780. For example, the transformed data objects may be
stored in a different storage system or service (e.g., a different
database system than the first data store). In some embodiments,
the second data store may be the same as the first data store. For
example, the transformed data object(s) may be stored in a
different location in the first data store (e.g., in a different
file directory). In at least some embodiments, an event may be
triggered by storing the transformed data object(s) in the second
data store. For example, the event may be another trigger event for
another ETL process. A request may be sent to update a data catalog
for the transformed data object(s) which may be detected,
triggering the other trigger event.
[0059] In some embodiments, a retry configuration or threshold may
be implemented for the ETL process. For example, if an ETL process
is allowed to execute (after satisfying the execution criteria) but
fails (e.g., in attempting to obtain data from the source data
object, to apply transformations to the source data object, or to
store transformed data objects), the retry configuration may direct
one ear more subsequent attempts to execute the ETL process. In
this way, the ETL process may be executed without waiting for the
detection of another trigger event, which may be beneficial in
scenarios where a chain of ETL processes are dependent upon the
completion of prior ETL processes.
[0060] The methods described herein may in various embodiments be
implemented by any combination of hardware and software. For
example, in one embodiment, the methods may be implemented by a
computer system (e.g., a computer system as in FIG. 8) that
includes one or more processors executing program instructions
stored on a computer-readable storage medium coupled to the
processors. The program instructions may be configured to implement
the functionality described herein (e.g., the functionality of
various servers and other components that implement the
network-based virtual computing resource provider described
herein). The various methods as illustrated in the figures and
described herein represent example embodiments of methods. The
order of any method may be changed, and various elements may be
added, reordered, combined, omitted, modified, etc.
[0061] Embodiments of event driven ETL processing as described
herein may be executed on one or more computer systems, which may
interact with various other devices. One such computer system is
illustrated by FIG. 8. In different embodiments, computer system
1000 may be any of various types of devices, including, but not
limited to, a personal computer system, desktop computer, laptop,
notebook, or netbook computer, mainframe computer system, handheld
computer, workstation, network computer, a camera, a set top box, a
mobile device, a consumer device, video game console, handheld
video game device, application server, storage device, a peripheral
device such as a switch, modem, router, or in general any type of
compute node, computing device, or electronic device.
[0062] In the illustrated embodiment, computer system 1000 includes
one or more processors 1010 coupled to a system memory 1020 via an
input/output (I/O) interface 1030. Computer system 1000 further
includes a network interface 1040 coupled to I/O interface 1030,
and one or more input/output devices 1050, such as cursor control
device 1060, keyboard 1070, and display(s) 1080. Display(s) 1080
may include standard computer monitor(s) and/or other display
systems, technologies or devices. In at least some implementations,
the input/output devices 1050 may also include a touch- or
multi-touch enabled device such as a pad or tablet via which a user
enters input via a stylus-type device and/or one or more digits. In
some embodiments, it is contemplated that embodiments may be
implemented using a single instance of computer system 1000, while
in other embodiments multiple such systems, or multiple nodes
making up computer system 1000, may be configured to host different
portions or instances of embodiments. For example, in one
embodiment some elements may be implemented via one or more nodes
of computer system 1000 that are distinct from those nodes
implementing other elements.
[0063] In various embodiments, computer system 1000 may be a
uniprocessor system including one processor 1010, or a
multiprocessor system including several processors 1010 (e.g., two,
four, eight, or another suitable number). Processors 1010 may be
any suitable processor capable of executing instructions. For
example, in various embodiments, processors 1010 may be
general-purpose or embedded processors implementing any of a
variety of instruction set architectures (ISAs), such as the x86,
PowerPC, SPARC, or MIPS ISAs, or any other suitable ISA. In
multiprocessor systems, each of processors 1010 may commonly, but
not necessarily, implement the same ISA.
[0064] In some embodiments, at least one processor 1010 may be a
graphics processing unit. A graphics processing unit or GPU may be
considered a dedicated graphics-rendering device for a personal
computer, workstation, game console or other computing or
electronic device. Modern GPUs may be very efficient at
manipulating and displaying computer graphics, and their highly
parallel structure may make them more effective than typical CPUs
for a range of complex graphical algorithms. For example, a
graphics processor may implement a number of graphics primitive
operations in a way that makes executing them much faster than
drawing directly to the screen with a host central processing unit
(CPU). In various embodiments, graphics rendering may, at least in
part, be implemented by program instructions configured for
execution on one of, or parallel execution on two or more of, such
GPUs. The GPU(s) may implement one or more application programmer
interfaces (APIs) that permit programmers to invoke the
functionality of the GPU(s). Suitable GPUs may be commercially
available from vendors such as NVIDIA Corporation, ATI Technologies
(AMD), and others.
[0065] System memory 1020 may be configured to store program
instructions and/or data accessible by processor 1010. In various
embodiments, system memory 1020 may be implemented using any
suitable memory technology, such as static random access memory
(SRAM), synchronous dynamic RAM (SDRAM), nonvolatile/Flash-type
memory, or any other type of memory. In the illustrated embodiment,
program instructions and data implementing desired functions, such
as those described above are shown stored within system memory 1020
as program instructions 1025 and data storage 1035, respectively.
In other embodiments, program instructions and/or data may be
received, sent or stored upon different types of
computer-accessible media or on similar media separate from system
memory 1020 or computer system 1000. Generally speaking, a
non-transitory, computer-readable storage medium may include
storage media or memory media such as magnetic or optical media,
e.g., disk or CD/DVD-ROM coupled to computer system 1000 via I/O
interface 1030. Program instructions and data stored via a
computer-readable medium may be transmitted by transmission media
or signals such as electrical, electromagnetic, or digital signals,
which may be conveyed via a communication medium such as a network
and/or a wireless link, such as may be implemented via network
interface 1040.
[0066] In one embodiment, I/O interface 1030 may be configured to
coordinate I/O traffic between processor 1010, system memory 1020,
and any peripheral devices in the device, including network
interface 1040 or other peripheral interfaces, such as input/output
devices 1050. In some embodiments, I/O interface 1030 may perform
any necessary protocol, timing or other data transformations to
convert data signals from one component (e.g., system memory 1020)
into a format suitable for use by another component (e.g.,
processor 1010). In some embodiments, I/O interface 1030 may
include support for devices attached through various types of
peripheral buses, such as a variant of the Peripheral Component
Interconnect (PCI) bus standard or the Universal Serial Bus (USB)
standard, for example. In some embodiments, the function of I/O
interface 1030 may be split into two or more separate components,
such as a north bridge and a south bridge, for example. In
addition, in some embodiments some or all of the functionality of
I/O interface 1030, such as an interface to system memory 1020, may
be incorporated directly into processor 1010.
[0067] Network interface 1040 may be configured to allow data to be
exchanged between computer system 1000 and other devices attached
to a network, such as other computer systems, or between nodes of
computer system 1000. In various embodiments, network interface
1040 may support communication via wired or wireless general data
networks, such as any suitable type of Ethernet network, for
example; via telecommunications/telephony networks such as analog
voice networks or digital fiber communications networks; via
storage area networks such as Fibre Channel SANs, or via any other
suitable type of network and/or protocol.
[0068] Input/output devices 1050 may, in some embodiments, include
one or more display terminals, keyboards, keypads, touchpads,
scanning devices, voice or optical recognition devices, or any
other devices suitable for entering or retrieving data by one or
more computer system 1000. Multiple input/output devices 1050 may
be present in computer system 1000 or may be distributed on various
nodes of computer system 1000. In some embodiments, similar
input/output devices may be separate from computer system 1000 and
may interact with one or more nodes of computer system 1000 through
a wired or wireless connection, such as over network interface
1040.
[0069] As shown in FIG. 8, memory 1020 may include program
instructions 1025, configured to implement the various methods and
techniques as described herein, and data storage 1035, comprising
various data accessible by program instructions 1025. In one
embodiment, program instructions 1025 may include software elements
of embodiments as described herein and as illustrated in the
Figures. Data storage 1035 may include data that may be used in
embodiments. In other embodiments, other or different software
elements and data may be included.
[0070] Those skilled in the art will appreciate that computer
system 1000 is merely illustrative and is not intended to limit the
scope of the techniques as described herein. In particular, the
computer system and devices may include any combination of hardware
or software that can perform the indicated functions, including a
computer, personal computer system, desktop computer, laptop,
notebook, or netbook computer, mainframe computer system, handheld
computer, workstation, network computer, a camera, a set top box, a
mobile device, network device, internet appliance, PDA, wireless
phones, pagers, a consumer device, video game console, handheld
video game device, application server, storage device, a peripheral
device such as a switch, modem, router, or in general any type of
computing or electronic device. Computer system 1000 may also be
connected to other devices that are not illustrated, or instead may
operate as a stand-alone system. In addition, the functionality
provided by the illustrated components may in some embodiments be
combined in fewer components or distributed in additional
components. Similarly, in some embodiments, the functionality of
some of the illustrated components may not be provided and/or other
additional functionality may be available.
[0071] Those skilled in the art will also appreciate that, while
various items are illustrated as being stored in memory or on
storage while being used, these items or portions of them may be
transferred between memory and other storage devices for purposes
of memory management and data integrity. Alternatively, in other
embodiments some or all of the software components may execute in
memory on another device and communicate with the illustrated
computer system via inter-computer communication. Some or all of
the system components or data structures may also be stored (e.g.,
as instructions or structured data) on a computer-accessible medium
or a portable article to be read by an appropriate drive, various
examples of which are described above. In some embodiments,
instructions stored on a non-transitory, computer-accessible medium
separate from computer system 1000 may be transmitted to computer
system 1000 via transmission media or signals such as electrical,
electromagnetic, or digital signals, conveyed via a communication
medium such as a network and/or a wireless link. Various
embodiments may further include receiving, sending or storing
instructions and/or data implemented in accordance with the
foregoing description upon a computer-accessible medium.
Accordingly, the present invention may be practiced with other
computer system configurations.
[0072] It is noted that any of the distributed system embodiments
described herein, or any of their components, may be implemented as
one or more web services. For example, nodes within an ETL system
may present ETL services to clients as network-based services. In
some embodiments, a network-based service may be implemented by a
software and/or hardware system designed to support interoperable
machine-to-machine interaction over a network. A network-based
service may have an interface described in a machine-processable
format, such as the Web Services Description Language (WSDL). Other
systems may interact with the web service in a manner prescribed by
the description of the network-based service's interface. For
example, the network-based service may define various operations
that other systems may invoke, and may define a particular
application programming interface (API) to which other systems may
be expected to conform when requesting the various operations.
[0073] In various embodiments, a network-based service may be
requested or invoked through the use of a message that includes
parameters and/or data associated with the network-based services
request. Such a message may be formatted according to a particular
markup language such as Extensible Markup Language (XML), and/or
may be encapsulated using a protocol such as Simple Object Access
Protocol (SOAP). To perform a web services request, a network-based
services client may assemble a message including the request and
convey the message to an addressable endpoint (e.g., a Uniform
Resource Locator (URL)) corresponding to the web service, using an
Internet-based application layer transfer protocol such as
Hypertext Transfer Protocol (HTTP).
[0074] In some embodiments, web services may be implemented using
Representational State Transfer ("RESTful") techniques rather than
message-based techniques. For example, a web service implemented
according to a RESTful technique may be invoked through parameters
included within an HTTP method such as PUT, GET, or DELETE, rather
than encapsulated within a SOAP message.
[0075] The various methods as illustrated in the FIGS. and
described herein represent example embodiments of methods. The
methods may be implemented in software, hardware, or a combination
thereof. The order of method may be changed, and various elements
may be added, reordered, combined, omitted, modified, etc.
[0076] Various modifications and changes may be made as would be
obvious to a person skilled in the art having the benefit of this
disclosure. It is intended that the invention embrace all such
modifications and changes and, accordingly, the above description
to be regarded in an illustrative rather than a restrictive
sense.
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